Decision Support System (DSS) Dashboard
The main issues:
Searching the internet for Business Intelligence (BI) tools, there are big name vendors with brilliant
webpages presenting great features and capabilities. This makes anyone wonder if CEO are
buying what are posted on these webpages or the claims and the promises posted by these vendors.
The main issues in building BI tools are the size and complexity of Big Data, the ever-changing
markets and clients, social media impact on the clients, the ever-changing
technologies, and the rest of the list is not small. This boils down to the following:
Storing and handling Big Data
Turning Big Data into manageable matrixes for faster and intelligent analysis
Making sense of Big Data, CRM, Social Media and market-clients trends
Build a GUI Decision Support System (DSS) Dashboard
Decision Support System (DSS) Dashboard
Intelligent Dashboards are the latest and the greatest tools used by CEO in
making decisions, plans, strategies, budgets and reports. How can we architect
an Intelligent Decision Support System (DSS) Dashboard for CEO which provides
real answers not just brilliant webpages with claims and promises.
Briefly Describe This Use Case
Big Data (structured and unstructured) is still a puzzle and making sense of Big Data
is the goal. Big Data need to be stored, summarized, processed, and analyzed. Data
should be presented in an effective format, otherwise, it would not have any
value. Our best answer is using matrixes and hash tables. The analogy of our approach
is the use of Shorthand or Stenography which increases speed and briefness of writing
as compared to longhand. With the same principle, tables and hash tables would be our
short hand of values found in Big Data. In short, we would turn data into a tabular
format. Tables are best suited for representing individual information and represent
both quantitative and qualitative information. Hash Tables can be used as indexes to
place holders of more data or processes.
CEO, stakeholders, directors and architects.
Dashboard is mainly used as a management tool to gauge performance and progress toward
business goals. Dashboards can be designed and developed to address a wide range of
objectives, from monitoring the practicality of a global organization's business strategy
to deliver a mix of operational, financial and project-specific information.
We are taking dashboard to another intelligent, dynamic and real time level. We architecting
dashboard with intelligent layers. Each layer is a service its successor. We also adding website
builder features, where CEO and their supportive teams can build a running website with
all the site gadgets and products details in real time with a push of a button.
Spring Replacement (The Elephant in The Room)
Our analysis in a nutshell is how to shorthand values stored in Big Data into a number
of manageable tables and hash tables for faster processes and analysis. These tables
must be kept running in memory, updated on a regular basis and stored or backed up for
reuse. We need to learn from that fact that Big Data size and complexity are manageable.
Our architect would create a number services and each service has a limited number of
matrixes and hash tables. We should make use of algorithms and abstract thinking in
building the matrixes and the hash tables.
Using Virtualization Objects:
Virtualization means to create a virtual version of a device or resource, such
as a server, storage device, network or even an operating system where the framework
divides the resource into one or more execution. We are architecting virtual servers and
virtual objects as well as virtual testing servers for testing these virtual objects.
Architecting Intelligent Objects:
How can we add Intelligent to Java Objects?
We are implementing matrixes and bit maps for fast processing using numeric values.
These processes include:
Adding steps-code-methods which collect the needed data for processing
Dynamic Business Rules
Dynamic Business rules main job is providing values, processes, sequence of execution and decision-making parameters
Collecting the actual values to be processed
We architecting Engines objects where each engine performs one task (a number of
mini-processes). We also architecting Service Objects which use a number of engines objects.
Using Bit maps and number for fast compression
Searching for ranges, limits and max values
Finding patterns and trends
Looking for patterns and trends
Thinking in abstract
From values, ranges, trends, frequencies and other parameters, we can conclude or forecast events or actions
Cross-reference of values would give us the ability to find errors and conclude or forecast events or actions
Tracking the success-failure of each processes and object
Decision-making based on dynamic business rules
Use Business Rules and weight-score values to make educated guess or decisions
Our Dashboard architect's main goal is to turn Big Data into data visualizing tool
to help CEO compete in today's competitive market. Our architect is to shorthand
values in big data into manageable data format. Then use this format in creating
CEO Data Visualizer. We are also taking dashboard to another intelligent, dynamic
and real time level. CEO and stakeholders and their supportive teams can actually
build an entire website with all its gadgets and products details. We using
Virtualization in the dashboard implementation.
DSS Dashboard Diagram - Figure #1
DSS Dashboard Diagram (Figure #1) presents how data flow through the systems and types of processes
and services are utilized to create an intelligent DSS Dashboard.
Input of Big Data:
Big Data is composed of Petabytes (1,024 terabytes) or exabytes (1,024 petabytes) of
data which consists of billions to trillions of records of millions of people. Structured
data is what databases are storing. Unstructured are files of different
types of data such as XML, Excel sheets, PDF, images, text, video, audio, ..etc. The
size and complexity of data is main issue. The following are some of Big Data input:
2. Market Research
5. Social Media
6. Structured Data
7. Unstructured Data
Virtual Matrixes and Hash tables:
To shorthand values in Big Data, we are proposing the use of the following
methodologies. First we need to group data values into target groups such as
personal, business, type of business, transactional processes, statistics, ..etc. We
can use abstract thinking in making decisions and predications. The data
selected for these target groups would be stored in data table and hash
tables. We are implementing hash tables as lookup values which may not be
numeric. These data tables and hash tables would be running as memory resident
objects which can be stored or backed up. Virtualization is an added feature
which helps in customization-building-deleting these objects.
It can also clones-copies these objects with speed.
The following are some of our approaches, methods and processes:
2. Abstract Thinking
5. Big Business
6. Transactional Processes
Virtual Engines Objects:
Once the target data object are created, then we need build what we call Engines. These
engines use the target data objects to perform the dashboard detailed tasks or
processes. Each engine performs only one task and no overlapping of tasks. These engines
should be documented and tested. These engines are also virtual objects. They are loosely
coupled and refactoring and transparency are also implemented. The following are engines
1. Social Media
5. Trends and tendencies
12. Ad Hoc handling
13. Strategic Planning
16. Key Performance Indicators
Virtual Services Objects:
Service Objects would be using different engines objects to create independent services. These
services objects would create loosely coupled services. Therefore the details of each services are performed
by a number of engines. Overlapping of usage of engines would help eliminate duplication of development
code and effort. The following are some of the virtual services objects:
3. Trends and tendencies
9. Ad Hoc handling
10. Strategic Planning
13. Key Performance Indicators
Virtual Business Objects:
Business Objects are the cloud dashboard services handlers. They would create the
services objects to handle the Virtual Cloud based Dashboard requests. They perform
the management of services.
Virtual Cloud based Dashboard:
Virtual Cloud based Dashboard is the virtual web cloud server running on the web. It
handle security using a number security components including firewalls.
GUI Frontend Data Visualizer:
GUI Frontend Data Visualizer is GUI frontend component which the end users would be using.
These can be implemented in a number of ways including Network-Attached Storage (NAS)
or and Storage area networks (SANs)